Error correction, sensory prediction, and adaptation in motor control.

By Reza Shadmehr, Maurice A. Smith, John W. Krakauer. Annual Review of Neuroscience, 2010

1,725 citations recorded by the source corpus.

Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.

Subfields: Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/fc91ba123a8242ed3b3ed7633b46204defa166b2

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Citations in the corpus

Dopamine in motivational control: rewarding, aversive, and alerting (2010, background): “All of these phenomena form a remarkable parallel to similar effects seen in sensory and motor adaptation (Braun et al., 2010; Fairhall et al., 2001; Shadmehr et al., 2010), suggesting that they may reflect a general neural mechanism for predictive learning.”
Computational neuroanatomy of speech production (2012, positive): “In addition to the parietal cortex, the cerebellum has long been implicated in internal models of motor control, including within the speech domain 18,40,76–79 , and the cerebellum has been specifically implicated as being part of a forward model 80,81 .”
Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition. (2016, positive): “In advanced control theoretical schemes, prediction is used, for example, to improve state estimation or to substitute missing (or delayed) feedback [11].”
elife.elifesciences.org RESEARCH ARTICLE Convergence of pontine and (2013, neutral): “Corollary discharge-driven predictions will contain some degree of error, which is thought to be computed by comparing pure sensory models against forward models (Mazzoni and Krakauer, 2006; Shadmehr et al., 2010).”
Neuroscience Needs Behavior: Correcting a Reductionist Bias. (2017, positive): “Motor learning is often studied using adaptation paradigms in which participants are subjected to an external perturbation, which causes systematic errors that are then corrected (Shadmehr et al., 2010).”
Neuroplasticity subserving motor skill learning (2011, positive): “These perturbations are more commonly introduced while subjects execute simple motor tasks, for instance, point-to-point ballistic reaching movements (Krakauer, 2009; Shadmehr et al., 2010; Seidler, 2010; Lalazar and Vaadia, 2008).”
How do forward models work? And why would you want them? (2013, positive): “The current trendsetters in predictive coding are the motor control crowd who have developed, empirically validated, and promoted the notion of internal forward models as a neural mechanism necessary for smooth, efficient motor control (Kawato, 1999; Shadmehr et al. 2010; Wolpert et al. 1995).”
Learning to expect the unexpected: Rapid updating in primate cerebellum during voluntary self-motion (2015, neutral): “(3) np g © 2 01 5 N at ur e A m er ic a, In c.”
Consensus Paper: The Cerebellum's Role in Movement and Cognition (2013, methodology): “In this computational scheme, prediction (forward modeling) gives clear advantages in terms of compensation of noise and delay in sensory feedback, cancelation of self-generated stimuli, and better state estimation [189, 190].”
A predictive network architecture for a robust and smooth robot docking behavior (2012, methodology): “This reinforcement learning algorithm is similar to motor adaptation according to error-prediction in biology [33, 34].”